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make transform_DDP more intuitive

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ykume
2023-05-03 11:07:29 +09:00
parent e1143caf38
commit 2fcbfec178
6 changed files with 8 additions and 8 deletions
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2
fine_tune.py
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@@ -229,7 +229,7 @@ def train(args):
unet, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(unet, optimizer, train_dataloader, lr_scheduler) unet, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(unet, optimizer, train_dataloader, lr_scheduler)
# transform DDP after prepare # transform DDP after prepare
text_encoder, unet, _ = train_util.transform_DDP(text_encoder, unet) text_encoder, unet = train_util.transform_if_model_is_DDP(text_encoder, unet)
# 実験的機能：勾配も含めたfp16学習を行う　PyTorchにパッチを当ててfp16でのgrad scaleを有効にする # 実験的機能：勾配も含めたfp16学習を行う　PyTorchにパッチを当ててfp16でのgrad scaleを有効にする
if args.full_fp16: if args.full_fp16:

6
library/train_util.py
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@@ -2897,9 +2897,9 @@ def _load_target_model(args: argparse.Namespace, weight_dtype, device="cpu"):
return text_encoder, vae, unet, load_stable_diffusion_format return text_encoder, vae, unet, load_stable_diffusion_format
def transform_DDP(text_encoder, unet, network=None): def transform_if_model_is_DDP(text_encoder, unet, network=None):
# Transform text_encoder, unet and network from DistributedDataParallel # Transform text_encoder, unet and network from DistributedDataParallel
return (encoder.module if type(encoder) == DDP else encoder for encoder in [text_encoder, unet, network]) return (model.module if type(model) == DDP else model for model in [text_encoder, unet, network] if model is not None)
def load_target_model(args, weight_dtype, accelerator): def load_target_model(args, weight_dtype, accelerator):
@@ -2922,7 +2922,7 @@ def load_target_model(args, weight_dtype, accelerator):
torch.cuda.empty_cache() torch.cuda.empty_cache()
accelerator.wait_for_everyone() accelerator.wait_for_everyone()
text_encoder, unet, _ = transform_DDP(text_encoder, unet, network=None) text_encoder, unet = transform_if_model_is_DDP(text_encoder, unet)
return text_encoder, vae, unet, load_stable_diffusion_format return text_encoder, vae, unet, load_stable_diffusion_format

2
train_db.py
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@@ -197,7 +197,7 @@ def train(args):
unet, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(unet, optimizer, train_dataloader, lr_scheduler) unet, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(unet, optimizer, train_dataloader, lr_scheduler)
# transform DDP after prepare # transform DDP after prepare
text_encoder, unet, _ = train_util.transform_DDP(text_encoder, unet) text_encoder, unet = train_util.transform_if_model_is_DDP(text_encoder, unet)
if not train_text_encoder: if not train_text_encoder:
text_encoder.to(accelerator.device, dtype=weight_dtype) # to avoid 'cpu' vs 'cuda' error text_encoder.to(accelerator.device, dtype=weight_dtype) # to avoid 'cpu' vs 'cuda' error

2
train_network.py
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@@ -262,7 +262,7 @@ def train(args):
network, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(network, optimizer, train_dataloader, lr_scheduler) network, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(network, optimizer, train_dataloader, lr_scheduler)
# transform DDP after prepare (train_network here only) # transform DDP after prepare (train_network here only)
text_encoder, unet, network = train_util.transform_DDP(text_encoder, unet, network) text_encoder, unet, network = train_util.transform_if_model_is_DDP(text_encoder, unet, network)
unet.requires_grad_(False) unet.requires_grad_(False)
unet.to(accelerator.device, dtype=weight_dtype) unet.to(accelerator.device, dtype=weight_dtype)

2
train_textual_inversion.py
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@@ -281,7 +281,7 @@ def train(args):
) )
# transform DDP after prepare # transform DDP after prepare
text_encoder, unet, _ = train_util.transform_DDP(text_encoder, unet) text_encoder, unet = train_util.transform_if_model_is_DDP(text_encoder, unet)
index_no_updates = torch.arange(len(tokenizer)) < token_ids[0] index_no_updates = torch.arange(len(tokenizer)) < token_ids[0]
# print(len(index_no_updates), torch.sum(index_no_updates)) # print(len(index_no_updates), torch.sum(index_no_updates))

2
train_textual_inversion_XTI.py
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@@ -315,7 +315,7 @@ def train(args):
) )
# transform DDP after prepare # transform DDP after prepare
text_encoder, unet, _ = train_util.transform_DDP(text_encoder, unet) text_encoder, unet = train_util.transform_if_model_is_DDP(text_encoder, unet)
index_no_updates = torch.arange(len(tokenizer)) < token_ids_XTI[0] index_no_updates = torch.arange(len(tokenizer)) < token_ids_XTI[0]
# print(len(index_no_updates), torch.sum(index_no_updates)) # print(len(index_no_updates), torch.sum(index_no_updates))